arXiv:2607.00274v1 Announce Type: new Abstract: Effective writing feedback is among the strongest drivers of student learning, yet producing it at scale is labor-intensive. LLMs offer a natural path to scaling writing support, but two gaps stand in the way: few public corpora capture how instructors actually deliver feedback in real classrooms, and no reliable method measures whether generated feedback aligns with what an instructor would write. We address both. SEFORA is a public corpus pairing instructor inline feedback with assignment prompts, rubrics, scores, and multi-draft revisions acro
The proliferation of Large Language Models (LLMs) has created a pressing need for robust frameworks to evaluate their performance in complex, nuanced tasks like generating educational feedback, which SEFORA addresses.
Evaluating the efficacy of LLM-generated feedback is crucial for integrating AI into educational systems at scale, potentially transforming how writing instruction and assessment are delivered.
The availability of a public corpus pairing instructor feedback with student work, alongside an LLM feedback evaluation framework, enables more rigorous development and deployment of AI-powered educational tools.
- · Educational technology providers
- · Students
- · Educators
- · AI researchers in NLP
- · Traditional writing feedback services (if they fail to adapt)
- · Institutions resistant to AI integration
This corpus and framework will accelerate research into AI-driven instructional feedback systems.
Educational institutions may begin widespread adoption of AI tools for providing writing feedback, leading to improved student outcomes and reduced instructor workload.
The democratization of high-quality writing feedback could significantly alter literacy rates and critical thinking skills across various educational levels globally.
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